Research Article

Journal of Orthoptera Research 2022, 311): 91-103

Relationships among body size components of three flightless
New Zealand grasshopper species (Orthoptera, Acrididae)

and their ecological applications

FABIO LEONARDO Meza-Joya!, MARY MorGAN-RICHARDS!, STEVEN A. TREWICK!

1 Wildlife & Ecology, School of Natural Sciences, Massey University, Private Bag 11-222, Palmerston North, New Zealand

Corresponding author: Fabio Leonardo Meza-Joya (f.1.mezajoya@ massey.ac.nz)

Academic editor: Maria-Marta Cigliano | Received 27 December 2021 | Accepted 18 March 2022 | Published 16 June 2022

http://zoobank.org/BCC37203-3F35-4897-97B 1-B3F841833F96

Citation: Meza-Joya FL, Morgan-Richards M, Trewick SA (2022) Relationships among body size components in three species of flightless New Zealand
grasshoppers (Orthoptera, Acrididae) and their ecological applications. Journal of Orthoptera Research 31(1): 91-103. https://doi.org/10.3897/jor.31.79819

Abstract

Body size is perhaps the most fundamental property of an organism
and is central to ecology at multiple scales, yet obtaining accurate estimates
of ecologically meaningful size metrics, such as body mass, is often
impractical. Allometric scaling and mass-to-mass relationships have been
used as alternative approaches to model the expected body mass of many
species. However, models for predicting body size in key herbivorous
insects, such as grasshoppers, exist only at the family level. To address this
data gap, we collected empirical body size data (hind femur length and
width, pronotum length, live fresh mass, ethanol-preserved mass, and dry
mass) from 368 adult grasshoppers of three flightless species at Hamilton
Peak, Southern Alps, New Zealand. We examined the relationships among
body size components across all species using linear and non-linear
regression models. Femur length and preserved mass were robust predictors
of both fresh mass and dry mass across all species; however, regressions
using preserved mass as a predictor always showed higher predictive power
than those using femur length. Based on our results, we developed species-
specific statistical linear mixed-effects models to estimate the fresh and
dry masses of individual grasshoppers from their preserved mass and
femur length. Including sex as an additional co-variate increased model
fit in some cases but did not produce better estimates than traditional
mass-to-mass and allometric scaling regressions. Overall, our results
showed that two easy-to-measure, unambiguous, highly repeatable, and
non-destructive size measures (i.e., preserved mass and femur length) can
predict, to an informative level of accuracy, fresh and dry body mass across
three flightless grasshopper species. Knowledge about the relationships
between body dimensions and body mass estimates in these grasshoppers
has several important ecological applications, which are discussed.

Keywords

allometric scaling, body mass, linear body dimension, mass-to-mass rela-
tionships, predictive models

Introduction

Organism body size is one of the most important axes in
ecology, as it is related to nearly all biological processes, from
individual performance to ecosystem function (Whitman 2008,
Chown and Gaston 2010). In insects, body size is closely linked to

physiological rates (e.g., metabolic and growth), life-history traits
(e.g., longevity and fecundity), and ecological attributes, such as
abundance, range size, and dispersal (Peters 1983, Siemann et al.
1996, Whitman 2008, Chown and Gaston 2010, Ehnes et al. 2011,
Stevens et al. 2012). Moreover, arthropod body size is central to
the contribution of individuals and communities to key ecosystem
processes and services, such as decomposition, carbon cycling,
primary productivity, pollination, predation, and herbivory (Cizek
2005, Barnes et al. 2018, Kendall et al. 2019). Therefore, changes
in the body size of a taxon reflect changes in resources that may
cascade across all levels of biological organization. For example,
body size differences are usually associated with individual
survival and fecundity, and changes in body size might alter
ecological processes, including trophic interactions, plant-animal
interactions, and food web connectivity (Peters 1983, Stang et al.
2009, DeLong et al. 2015, Horne et al. 2018).

Adult body size in Orthoptera is generally expressed in terms
of length and mass, each of which is controlled by both genet-
ic and environmental factors that operate through molecular
and physiological mechanisms (Nijhout 2003, Whitman 2008,
Chown and Gaston 2010). Although length and mass are often
correlated, each captures a different aspect of an organism's size
and is subject to different selective pressures during an organism’s
lifespan (Gaston and Blackburn 2000). Insect structural body size
(e.g., length dimensions) is determined during development by
gene-environment interactions, whereas adult body mass addi-
tionally varies through time depending on environmental factors,
for example, reproductive phase and nutritional status (Whitman
2008, Chown and Gaston 2010, Knapp and Knappova 2013).
Despite this fact, body mass- and linear-based estimates are often
used interchangeably as measures of adult body size in ecological
research (Chown and Gaston 2010). Decisions on the body size
measure used in a particular study should be made cautiously and
considering the research question and species (Gaston and Black-
burn 2000, Moretti et al. 2017).

Body mass is the most meaningful size metric, as it is directly
linked with metabolic rate and is affected by environmental con-
ditions (Gaston and Blackburn 2000, Sohlstr6m et al. 2018).
Therefore, fresh (live) mass is preferred to relate body size to a range

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

92

of functional and ecological attributes, such as metabolism, move-
ment, and abundance (e.g., Chown and Steenkamp 1996, Meehan
2006, Ehnes et al. 2011, Hirt et al. 2017). In some instances, however,
dry mass is recorded to estimate, for example, organism biomass,
since variation from water content is reduced (e.g., Sage 1982, Cres-
sa 1999, Sabo et al. 2002, Gilbert 2011, Penell et al. 2018). While
body mass is a useful predictive trait for many ecosystem processes,
measuring individual arthropod body mass is a time-consuming
and tedious process (Johnston and Cunjak 1999, Ekl6f et al. 2017,
Sohlstr6m et al. 2018, Kendall et al. 2019). Moreover, collection and
storage methods often prevent the direct determination of mass es-
timates, especially when specimens are damaged (e.g., loss of ap-
pendages) or when subject to chemical preservation that causes un-
predictable mass change (Johnston and Cunjak 1999, Wetzel et al.
2005, Chown and Gaston 2010, Moretti et al. 2017). As a result, most
ecological studies on insects rely on more easily measured body di-
mensions (e.g., body length) as proxies for body size (Chown and
Gaston 2010). Many insect collections are composed of specimens
preserved in ethanol, and these collections provide an important
source of information about organismal change over time if we can
convert preserved mass to biologically meaningful measures.

Allometric scaling rules applied to co-varying traits can be used
to predict an organism’s body mass based on an easy-to-obtain
body length measurement, thus avoiding the use of problematic
body mass estimators (Johnston and Cunjak 1999, Moretti et al.
2017, Pennell et al. 2018, Kendall et al. 2019). Scaling equations
have proven to be powerful tools for the prediction of body mass
for a wide range of insect taxa based on different linear metrics
(e.g., Rogers et al. 1977, Schoener 1980, Johnston and Cunjak
1999, Sabo et al. 2002, Garcia-Barros 2015, Kendall et al. 2019).
These equations rely on regression parameters estimated for
length-mass relationships, which are often subject to intersexual
allometric differences (Hagen and Dupont 2013, Kendall et al.
2019). Incorporating sexual size dimorphism data into scaling
relationships, and thus their regression parameters, is crucial to
overcome this limitation (e.g., Kendall et al. 2019). Despite the
broad application of allometric scaling in ecological research,
there are surprisingly few studies providing regression parameters
for estimating the body mass of key herbivorous taxa, such
as grasshoppers (but see Schoener 1980, Sabo et al. 2002 for
allometric equations at the ordinal level).

Short-horn grasshoppers (Orthoptera: Acrididae) areamong the
most diverse (> 6,700 described species) and ubiquitous fauna of
grassland ecosystems around the world (Uvarov 1966, Latchininsky
et al. 2011, Song et al. 2018) contributing, in some cases, to more
than half of the total above-ground arthropod biomass (Gillon
1983, Song et al. 2018). The endemic short-horn grasshoppers of
Aotearoa New Zealand occur widely, but are especially abundant
in alpine habitats (Bigelow 1967, Trewick 2001, Trewick 2008,
Trewick and Morris 2008, Koot et al. 2020). As major invertebrate
herbivores in native grassland ecosystems (Batcheler 1967, White
1975), these grasshoppers might play a major role in structuring
plant communities and regulating ecosystem function via plant
productivity, competition, and nutrient cycling (Olff and Ritchie
1998, Belovsky and Slade 2000, Moretti et al. 2013, Deraison et
al. 2015). Given the ecological importance of grasshoppers, the
determination of allometric scaling relationships provides an
opportunity to explore ecologically important traits and variations
that are otherwise difficult to measure.

Body size data have been accumulated for New Zealand grass-
hoppers mostly as linear dimensions: hind femur length and
width, and pronotum length (e.g., Batcheler 1967, Staples 1967,

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

Bigelow 1967, Mason 1971; but see Dowle et al. 2014, Carmelet-
Rescan et al. 2021). However, the suitability of these measures as
predictors of body size and their relationship with other body
mass estimates have not been tested. A key feature of grasshop-
pers is the use of jumping in locomotion and predator avoidance
(Queathem 1991), and this is especially true for flightless species
such as those found in New Zealand. Therefore, the size of the
hind jumping leg may be closely related to other size components
and, thus, to overall body size. The marked sexual size dimor-
phism of most grasshoppers might compound intraspecific dif-
ferences in the relationships among body size components. Here,
we examined these relationships focusing on three brachypterous
and flightless species of the endemic alpine radiation of Ka Tiritiri-
o-te-moana, the Southern Alps (Bigelow 1967, Trewick and Mor-
ris 2008, Koot et al. 2020; Fig. 1A-C): Brachaspis nivalis (Hutton,
1987), Paprides nitidus Hutton, 1987, and Sigaus australis (Hutton,
1987). First, we quantified the effects of short-term ethanol pres-
ervation by describing the weight change over 120 days. Then, we
examined scaling ratios to assess the predictive power of preserved
mass for both fresh and dry masses. We also analyzed intraspecific
length-mass relationships over an elevation gradient to account,
at least partially, for environmental variation in body size. Based
on our results, we developed species-specific statistical models to
estimate the fresh and dry mass of individual grasshoppers from
their preserved mass and hind femur length. Overall, our models
showed high predictive power such that body mass estimates de-
rived from them can be used to test mechanistic hypotheses for
shifts in morphological and ecological traits related to body size.

Materials and methods

Specimen collection and measurements.—A total of 368 complete
adult specimens (no missing appendages) representing three grass-
hopper species (B. nivalis 614, 719; P. nitidus 734, 739; S. australis
423, 4892) were collected on Hamilton Peak in the Craigieburn
Range, New Zealand (-43.129, 171.688; WGS84). Sampling was
done by hand, capturing grasshoppers disturbed by walking at five
sites at ~100 m elevation intervals (BR1 to BR5) from 1,383 to
1,817 m asl, to capture as much local variation in body size as
possible. Species and sex were recorded from live specimens in
the field and were later corroborated upon processing based on
morphological features (e.g., body color pattern, pronotum shape,
and body shape and size) following Bigelow (1967). Maturity and
sex were determined using the size and shape of the tegmina and
terminalia (Bigelow 1967).

Grasshoppers were weighed alive after cooling to 4°C, then fro-
zen overnight before being preserved in 95% ethanol for DNA pres-
ervation. Specimens were weighed using a Sartorius Quintix35-1S
digital scale (Sartorius Lab Instruments GmbH & Co, Goettingen,
Germany) accurate to 0.001 g. We measured the left hind femur
length (hereafter femur length) and width (hereafter femur width),
and pronotum length of specimens (Fig. 1D) using an Olympus
SZX7 stereomicroscope with Olympus SC100 image capture and
Olympus cellSens Dimension v1.6 software (Olympus Corpora-
tion, Tokyo, Japan). These measures were chosen because they
are commonly used proxies for body size in grasshoppers (e.g.,
Bigelow 1967, Mason 1971, Harris et al. 2012, Yadav et al. 2018).

To quantify the effects of our preservation method on body
mass estimates, we remeasured the body mass of all specimens
after two and four months of storage in ethanol. Once all other
measurements were completed, a random subsample of 50 speci-
mens of each species (25 males and 25 females) were dried in an

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

A. Brachaspis nivalis female; B. Paprides nitidus female; C. Sigaus australis male; D. Body dimensions used as proxies of overall body size
in this study: morphometric data were collected for hind femur length (FL), hind femur width (FW), and pronotum length (PL).

oven at 60°C for at least 96 h, until their mass ceased to change,
and were then weighed. To assess measurement repeatability, we
randomly selected five males and five females of each species and
remeasured and reweighed them three times in random order.

Data analysis and model structures.—Repeatability (R) was calculated
independently for species and sexes with the R package rptR
(Stoffel et al. 2017), using specimen as a grouping term. The ratio
of intra-observer variance (i.e., R) was calculated as the among-
group variance (VG) over the sum of group-level and within-group
(residual) variance (VR): R = VG / (VG + VR). Confidence intervals
(95%) around repeatability values were estimated using 1,000
parametric bootstrap iterations. The effect of preservation in 95%
ethanol on specimen body mass was examined by comparing
the mass of individuals when live (fresh mass) and after ethanol
preservation for two and four months. We also examined the
frequency distributions of differences in body mass before and after
preservation for each species. As the shape of the size-frequency
distribution was almost identical for both preserved states (Fig. 2),
we used a Wilcoxon signed-rank test to analyze overall and sex-
specific differences between fresh mass and preserved mass after
four months of preservation (hereafter preserved mass), pooling
data from all species. For these analyses, a non-parametric
approach was preferred, as mass difference between live and
4-month preserved specimens was not normally distributed when
considered together. Statistical tests were implemented using the R
package rstatix version 0.7.0 (Kassambara 2021).

We explored mass-to-mass ratios between ethanol preserved
mass (after four months of preservation, PM), and both fresh mass

(FM) and dry mass (DM) for each species, using model II regres-
sions with standardized major axis (SMA) in the R package smatr
version 3.4-8 (Warton et al. 2012). We performed SMA regres-
sions by (i) including an intercept term (i.e., not forced through
the origin) under the robust outlier option and (ii) assuming that
changes in any body mass metric is reflected in the other metric, as
measurements came from the same specimens (y = 0 when x = 0),
and forcing the intercept through the origin (i.e., zero-intercept).
We also tested fora common slope between sexes and among sites
(i.e., elevation) with an ANCOVA-like test, using the slopes esti-
mated in SMA regressions (Warton et al. 2012). Since preserved
mass was closely related to the other measures of mass (R? = 0.913,
p < 0.001; for additional details see Results), we specified a series
of species-specific linear mixed-effects (LMM) models to predict
FM and DM as a function of PM using the R package Ime4 version
1.1-27.1 (Bates et al. 2015). This approach allowed us to account
for sex- and site-specific differences in body mass by including sex
as an additional fixed effect and as an interaction term with pre-
served mass, elevation as a random intercept, and preserved mass
as arandom slope.

We used ordinary least squares (OLS) regressions in R base
(R Core Team 2020) to compare body dimensions (femur
length = FL, femur width = FW, and pronotum length = PL) as
predictors of body mass components (i.e., FM and DM) using log-
transformed data. For each species, we estimated and compared the
slopes of fitted lines between sexes using the R package emmeans
version 1.6.2-1 (Lenth 2021). As the strength of relationships varied
between sexes and in some instances presented apparent deviations
from linearity (see Results), we fitted sex-specific non-linear mod-

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

94

S. australis

eT ne i

P. nitidus

Tt

B. nivalis

PT AT

0.0 0.5 1:5

Body mass (g)

Density

004 0.06
Mass difference (g)

Fig. 2. A. Density distributions of body mass in three flightless
New Zealand grasshopper species when alive (turquoise) and af-
ter ethanol-preservation for two (dark yellow) and four months
(black); B. The distribution of the difference in mass between live
and 4-month preserved specimens pooled for all three species and
partitioned by sex. Mean values for male (-0.012 g) and females
(-0.029 g) are indicate by dashed lines. Marginal rug indicates in-
dividual observations of body mass.

els (Knell 2009) to analyze the shape of the scaling relationship.
Five models were compared using Akaike’s Information Criteria
(AIC): (i) quadratic, (ii) logistic, (iii) four-parameter logistic, (iv)
Weibull growth function, and (v) power function models. Models
were fitted on untransformed variables (Packard 2011) using base R
(R Core Team 2020) and the R package aomisc version 0.647 (Ono-
fri 2020). We chose femur length for the following analyses because
it was highly correlated with all other body dimensions (Pearson’s
R > 0.924, p < 0.001) and easier to measure consistently, as indi-
cated by our repeatability analysis (Suppl. material 1: Appendix 1).

We further explored scaling relationships between FL and both
FM and DM using model II regressions SMA including only an
intercept term (i.e., not forced through the origin), as the femur
length of adult insects does not change in response to changes
in body mass (Whitman 2008, Chown and Gaston 2010, Bailey
et al. 2020). We also specified LMMs using FL as a predictor of
both FM and DM, using homologous model structures as defined

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

previously for mass-to-mass modeling, to account for sex- and site-
specific differences in trait variability. These approaches were cho-
sen because sex-specific linear models generally performed as well
as or better than non-linear models (AAIC < 1.95), although when
predicting dry mass for females of B. nivalis, the quadratic model
performed slightly better than the linear model (AAIC = 2.61). For
model formulation, we used log-transformed values because static
allometric relationships explored here are generally well-described
by a power function (y = ax’), which is linearized when log-trans-
formed: In (y) = In (a) + B x In (x) + e, where y = dry mass, « = in-
tercept, B = allometric coefficient, and x = linear size proxy.

The best-fitted models (both allometric and LMMs) were
selected using Akaike’s information criterion corrected for
sample size (AlCc) and Akaike weight (wi) using the R package
AlCcmodavg version 2.3-1 (Mazerolle 2020). Models with
AAICc < 2 were considered equally supported by the data, while
models with AAICc > 2 were considered to show substantial
differences (Burnham and Anderson 2002). The Akaike weight
(wi) was interpreted as the probability that model i was the best
model given all evaluated models and data available (Burnham
and Anderson 2002). For all models, the goodness of fit was
examined by calculating conditional R’ using the R package
MUMIn version 1.43.17 (Barton 2020). The statistical significance
of fixed and random effects was examined for the best-fitted
models using the R package ImerTest version 3.1-3 (Kuznetsova
et al. 2017). Assumptions of model fit were met for all models as
indicated by diagnostic plots of residuals.

Testing model accuracy.—We predicted fresh and dry body mass for
368 grasshopper specimens using mass-to-mass ratios, scaling re-
gressions, and parameters from the best-fitted LMMs. We then test-
ed the relationship between measured and predicted values using
model II regressions with a major axis approach using the R pack-
age Imodel2 version 1.7-3 (Legendre 2018). This method is appro-
priate when comparing empirical observations to model predic-
tions (Legendre and Legendre 2012). The statistical significance of
relationships was tested using one-tailed permutation tests (with
1,000 permutations), and the strengths of the relationships were
determined by model R? values. Observed relationships were also
compared to the ideal x = y association where estimated = meas-
ured by calculation of 95% confidence intervals around the esti-
mated slope. The accuracy of our predictions was also estimated
using the root-mean-square error (RMSE) between the observed
and predicted values, using the R package Metrics version 0.1.4
(Hamner and Frasco 2018). All analyses were performed using R
4.0.3 (R Core Team 2020).

Results

We found high measurement consistency (R > 0.970), although
the degree of repeatability differed among body size proxies,
species, and sexes, reflecting the relative size of the values (Suppl.
material 1: Appendix 1). The highest mean repeatability was
recorded for the larger traits (femur length R = 0.9990 + 0.0001 SD,
preserved mass R = 0.9985 + 0.0001 SD), the larger species (B. nivalis
R = 0.9941 + 0.0082 SD and S. australis R = 0.9941 + 0.0094 SD
compared to P. nitidus R = 0.9912 + 0.0147 SD), and the larger
sex (females R = 0.9953 + 0.0068 SD compared to males
R = 0.9907 + 0.0148 SD). Overall, grasshopper specimens weighed
significantly less after four months in ethanol than when they were
alive (Wilcoxon’s test p < 0.001; Fig. 2A), although differences were
small (4.606% + 2.705 SD). On average, the larger female specimens

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

lost more weight than the male specimens (Wilcoxon's test p < 0.001;
Fig. 2B; see Suppl. material 1: Appendix 2 for species details).

There were strong and significant relationships between pre-
served mass (PM) and both fresh mass (FM, R? = 0.997, p < 0.001)
and dry mass (DM, R? => 0.913, p < 0.001) in all species (Fig. 3; Sup-
pl. material 1: Appendix 3). No significant differences in slopes
were indicated by the ANCOVA-like test for the two sexes, but site
differences were found when predicting DM as a function of PM
in S. australis (Suppl. material 1: Appendix 3). Estimated ratios of
preserved to fresh mass (mean ratio = 1.041 + 0.005 SD) and pre-
served to dry mass (mean = 0.310 + 0.008 SD) were similar for all
species (Table 1). All LMMs including co-variables exhibited simi-
lar overall predictive power as judged by their fitting scores (Table
2). When predicting fresh mass as a function of preserved mass,
the PM-only fixed-effect model incorporating site as a random ef-
fect (FM~PM+(1|Site)) outperformed other models for all species,
except B. nivalis (Table 2a). For this species, one of the models ac-
counting for sexual dimorphism exceeded the baseline model (i.e.,
FM~PM+(1|Site)) in terms of AICc (AAICc = 3.47, Awi = 0.54) but
not R’ (AR? = 0.001). In contrast, when predicting dry mass, one
of the models accounting for sexual dimorphism and site differ-
ences (FM~PM+Sex+(PM|Site)) surpassed other models for all
species (Table 1b) except B. nivalis. In this species, the PM-only
fixed-effect model outperformed models including sex in terms of
AlCc (AAICc = 2.47, Awi = 0.51) but not R? (AR? = 0.000). Fixed
effects were significant in all best-fitted models (p > 0.001), yet

a

7)

w

La]

=

=

w)

o

=

Le
B. nivalis oo
Ponitidus =
S. australis ==

0.3 0.6 09 12 0.3
Preserved mass (g)

a

rT)

)

Lit

£

on

—

a

B. nivalis ==
P onitidus =
S. australis =

03 0.6 09 1 03
Preserved mass (g)

95

the random effect (i.e., site) was only significant when predict-
ing FM for S. australis (p > 0.001; Suppl. material 1: Appendix 4).
All LMMs outperformed the null models (i-e., FMv1+(1|Site) and
DM~1+(1|Site)) in their predictive power (Table 2).

As expected, there was a strong and significant correlation
(Pearson's R< 0.893, p < 0.001) among all body size measures, with
pairwise comparisons involving femur length (FL) having the high-
est correlation coefficients (Pearson’s R > 0.924, p < 0.001; Suppl.
material 1: Appendix 5). All body dimensions exhibited strong and
significant linear relationships with both fresh mass (R? = 0.938,
p<0.001) and dry mass (R? = 0.887, p< 0.001), although the strength
of these relationships differed between sexes and, in some cases,
appeared nonlinear (Suppl. material 1: Appendix 5). Differences
in slopes between sexes were subtle for all species, and a significant
difference was only detected when predicting FM in the function
of pronotum length (PL) for S. australis (p = 0.007, Suppl. material
1: Appendix 5). Comparisons of sex-specific models showed that,
in most cases, linear models performed as well as or better than
alternative non-linear models. However, slight deviation from lin-
earity was detected when predicting DM for female B. nivalis, where
an allometric quadratic model performed marginally better than
a linear model for females (AAIC = 2.61), although both models
were comparable for males (AAIC = 1.88). Scaling relationships be-
tween body mass estimates and femur length were generally well-
described by a power function (Suppl. material 1: Appendix 6).
The coefficients from SMA regressions were similar for all species

Female -
Male wees

0.6 0.9 12 0.3 0.6 0.9 1.2
Preserved mass (g)

Preserved mass (g)

Female =
Male aie

0.6 0.9 1 0.3 06 0.9 le
Preserved mass (g)

Preserved mass (g)

Fig. 3. Mass-to-mass relationships in three flightless New Zealand grasshopper species showing the influence of elevation and sexual
dimorphism. Fresh mass—preserved mass (A-C) and dry mass-preserved mass (D-F). Sample sites (BR1 to BR5) indicating five sites
in ~100-m elevation intervals from 1,383 to 1,817 m asl. Lines represent the best-fit from standardized major axis regressions. Credible
intervals are omitted for clarity. Some regression lines overlie each other.

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

96

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

Table 1. Mass-to-mass ratios for predicting both fresh and dry mass from preserved mass in three flightless New Zealand grasshopper
species. Regression parameters based on standardized major axis regressions and their confidence intervals (95% CI) are shown.

Species SMA Intercept,

eee ee a a a ee) a eee

(a) Preserved mass to fresh mass (PM:FM)

Brachaspis nivalis 0-intercept 0.000
Brachaspis nivalis intercept O:009% Goi oio13y
Paprides nitidus 0-intercept 0.000
Paprides nitidus intercept 0:000, 55630008)
Sigaus australis 0-intercept 0.000
Sigaus australis intercept 01002, « eardlo0e)
(b) Preserved mass to dry mass (PM:DM)

Brachaspis nivalis 0-intercept 0.000
Brachaspis nivalis intercept D008 eeciavetaiss
Paprides nitidus 0-intercept 0.000
Paprides nitidus intercept 01001 +o 0.006)
Sigaus australis 0-intercept 0.000
Sigaus australis intercept -0.009

-0.019, 0.001

Slope, R? p-value Ratio
1045 aig 0.999 < 0.001 FM=1.045PM
LOSOh a tien 0.997 < 0.001 FM=1.030PM
10455 Fetes 0.999 < 0.001 EM=1.045PM
1045 baa anaes 0.998 < 0.001 FM=1.045PM
1.O4Pby saa 0.999 < 0.001 FM=1.042PM
1039 a semne: 0.998 < 0.001 EM=1.039PM
C2960 | aH ach 0.986 < 0.001 DM=0.296PM
O08. Jerre 0.913 < 0.001 DM-=0.308PM
OSI peeeiess 0.993 < 0.001 DM=0.316PM
OIC SE oat 0.969 < 0.001 DM=0.310PM
0.308% etc fists 0.987 < 0.001 DM-=0.308PM
O82 Van ss 0.959 < 0.001 DM=0.321PM

Table 2. Model selection showing the best-fitted models (AICc in
bold) for predicting both fresh mass and dry mass from preserved
mass in three New Zealand flightless grasshopper species. Abbrevia-
tions: K = number of parameters, AICc = Akaike’s information criteri-
on corrected for sample size, wi = Akaike weight, LL = Log-Likelihood,
R? = marginal R?. Model parameters of the best-fitting models (AAICc
< 2) used for predictions are shown in Suppl. material 1: Appendix 4.

Species Model formulae K AICc AAICc wi LL R?

(a) fresh mass (FM) as a function of preserved mass (PM)

Brachaspis nivalis FM~PM+Sex+(1|Site) 5 -808.58 0.00 0.66 409.53 0.997
FM~PM+Sex+(PM|Site) 7 -807.01 1.57 0.23 410.96 0.997
FM~PM*Sex+(1|Site) 6 -806.38 2.20 0.22 409.53 0.997

FM~PM+(1|Site) 4 -805.11 3.47 0.12 406.71 0.996
FM~1+(1|Site) 3 -64.11 744.47 0.00 35.15 0.112

Paprides nitidus FM~PM+(1|Site) 4 -937.02 0.00 0.67 472.65 0.998
FM~PM+Sex+(1|Site) 5 -934.87 2.14 0.23 472.65 0.998
FM~PM*Sex+(1|Site) 6 -933.26 3.75 0.10 472.93 0.998
FM~PM+Sex+(PM|Site) 7 -931.53 5.49 0.04 473.17 0.998

FM~1+(1|Site) 3-35.70 901.31 0.00 20.94 0.000

Sigaus australis FM~PM+(1|Site) 4 -566.34 0.00 0.50 288.35 0.999
FM~PM+Sex+(1|Site) 5 -565.69 0.65 0.36 287.41 0.999
FM~PM*Sex+(1|Site) 6 -563.69 2.65 0.13 288.20 0.999
FM~PM+Sex+(PM|Site) 7 -561.04 5.31 0.03 288.20 0.999

FM~1+(1|Site) 3 69.40 635.75 0.00 -31.56 0.000

(b) dry mass (DM) as a function of preserved mass (PM)

Brachaspis nivalis DMx«PM:+(1|Site) 4 -259.16 0.00 0.73 134.03 0.915
DMxPM+Sex+(1|Site) 5 -256.70 2.47 0.21 134.03 0.915
DMxPM*Sex+(1|Site) 6 -254.14 5.02 0.06 134.05 0.915
DM~PM+Sex+(PM|Site) 7 -251.39 7.77 0.01 134.03 0.915

DM~1+(1|Site) 3 -139.34 119.82 0.00 72.93 0.000

Paprides nitidus DM~PM+Sex+(PM|Site) 7 -300.56 0.00 0.67 158.65 0.981
DMx~PM+Sex+(1|Site) 5 -298.31 2.25 0.22 154.85 0.976
DM~PM*Sex+(1|Site) 6 -296.03 4.53 0.07 155.02 0.976

DM~PM:+(1|Site) 4 -294.75 5.81 0.04 151.83 0.972
DM~1+(1|Site) 3 -125.73 174.83 0.00 66.13 0.000

Sigaus australis DM~«PM+Sex+(PM|Site) 7 -258.46 0.00 0.59 137.53 0.979
DMx~PM+Sex+(1|Site) 5 -257.21 1.25 0.31 134.27 0.974
DMxPM*Sex+(1|Site) 6 -254.85 3.62 0.10 134.38 0.974

DM«PM:+(1|Site) 4 -241.01 17.46 0.00 124.94 0.964
DM~1+(1|Site) 3 -78.24 180.22 0.00 42.38 0.000

when scaling the relationship between FL and both FM and DM
(Table 3; Fig. 4). Most LMMs including co-variables displayed com-
parable overall predictive ability as judged by their fitting scores
(Table 4). In general, models accounting for sexual dimorphism

outperformed other models for all species, although in a few cases,
parameters from equally supported baseline models (e.g., FL-only
fixed-effect, AAICc < 2) led to more accurate body mass predictions
(Table 4). Fixed effects were significant in all best-fitted models (in
all cases p > 0.001, but p = 0.048 when predicting fresh mass for B.
nivalis), but the random effect (i.e., site) was not significant for any
model (p > 0.001; Suppl. material 1: Appendix 4). All formulated
LMMs outperformed the null models (i-e., In(FM)~1+(1|Site) and
In(DM)~1+(1|Site)) in their predictive power.

We found that predicted body mass (both fresh and dry mass)
was significantly correlated with empirical measurements; however,
using PM as a predictor led to the most accurate estimates (Fig. 5).
In all cases, the relationship between estimated and measured
body mass was not significantly different from a 1:1 relationship,
with > 89% of the variation explained (Table 5). The range of pre-
diction error (RMSE) was near identical for body mass predictions
obtained from mass-to-mass ratios, scaling regressions, and LMMs.
When using PM as a predictor, FM estimates from PM:FM ratios
were marginally more accurate than those from LMMs (RMSE
= 0.011 g and 0.012 g, respectively). In contrast, LMMs were slightly
more accurate than PM:DM ratios when predicting DM (RMSE
= 0.014 g and 0.017 g, respectively). However, the range of predic-
tion error was considerably higher when using FL as a predictor. For
FM estimates, predictions based on SMA scaling relationships were
marginally more accurate than those from LMMs (RMSE = 0.048 g
and 0.050 g, respectively), but when predicting DM, prediction er-
rors were identical using both methods (RMSE = 0.025 g).

Discussion

A key source of variation in morphological traits is measure-
ment repeatability, which is inherently related to the statistical
power of analyses based on those measurements (Bailey and By-
res 1990, Wylde and Bonduriansky 2021). We found the highest
repeatability for larger traits compared to smaller traits (e.g., femur
length vs femur width), and the larger sex (female vs male) when
pooling values for all size proxies and species. The effect of sex on
repeatability was less clear when considering individual traits, sug-
gesting that measurement repeatability in these species depends
on other factors such as species size, trait size, and their interac-
tions rather than sex alone. As noted by Bigelow (1967), measure-
ment repeatability in these grasshoppers decreases in traits with
rounded boundaries, such as femur width, and in traits where
margins are highly variable in shape, such as pronotum length.

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

Table 3. Length-mass scaling coefficients for predicting both fresh
and dry mass from femur length in three flightless New Zealand
grasshopper species. Regression parameters based on standard-
ized major axis regressions and their confidence intervals (95%
CI) are shown.

Species Model Intercept, Slope, R? p-value
formulae

(a) fresh mass (FM) as a function of femur length (FL)
Brachaspis nivalis In(FM)~In(FL) — -7.754) 095, 752) 2:696(.695, 2768) 9-965 < 0.001
Paprides nitidus In(FM)wIn(FL) — -8.914/ 5045, 6733) 3-090 4043157) 9-976 < 0.001
Sigaus australis —\n(FM)«In(FL) “9:3 84 793, 93395) . 2:2 Pesnaaiy C982 <= 0.001
(b) dry mass (DM) as a function of femur length (FL)
Brachaspis nivalis In(DM)~In(FL) -9.234 (9747 2) 2-778. 585,986) 0-898 < 0.001
Paprides nitidus In(DM)~In(FL) -10.077; 15 504,5.650) 3-O7 09,3282) 9-953 < 0.001
Sigaus australis \In(DM)eIn(FL) -11.423 (4, 515 10.997) 3-339 33573731) 0-939 < 0.001

In addition, the orientation of specimens to the focal plane of the
microscope can result in parallax error that is expected to be more
pronounced for small structures that are difficult to measure (e.g.,
Wylde and Bonduriansky 2021). We found larger traits, such as
femur length, could be measured with relatively little error com-
pared to smaller features (femur width and pronotum length) that
were subject to more parallax error. Measurement repeatability
was also higher for the larger body mass measures (fresh and pre-
served mass) compared to dry specimens (dry mass), which had
small values that were sensitive to variation in humidity. Dried

In fresh mass (g)

B. nivalis ===

P nitidus
S. australis -z

entee

2.6 2.8
In femur length (mm)

In dry mass (g)

B. nivalis =

P nitidus
S. australis =

ot

2.6 2.8
In femur length (mm)

In femur length (mm)

In femur length (mm)

97

specimens become slightly hydrated during weighing, resulting
in increased errors in measurement. Body length is a widely used
linear metric, but we found it unreliable in grasshoppers, as ab-
domen size varied considerably with body condition, including
reproductive state (Hochkirch and Gréning 2008, Garcia-Navas
et al. 2017). Furthermore, the extent of telescoping of abdominal
segments (Bigelow 1967) and distortion during preservation are
additional sources of measurement error (Garcia-Barros 2015).
Collecting and storing insects in chemical fluids, such as etha-
nol, has the potential to alter their body mass (Moretti et al. 2017,
Penell et al. 2018), thus limiting their use in ecological studies
that require accurate body mass data (Leuven et al. 1985, Chown
and Gaston 2010). We found that the weight loss of 95% ethanol-
preserved specimens was largely restricted to the first two months
of preservation after which weight stabilized, and only minimal
differences were recorded (Suppl. material 1: Appendix 2). The
high ethanol concentration (i.e., 95%) used here to also protect
DNA could explain these results, as it would speed the leaching
of water from tissues. Studies of aquatic insects show similar re-
sponses, with weight loss mostly limited to the first four weeks
after preservation (e.g., Stanford 1973, Leuven et al. 1985, Cressa
1999, Wetzel et al. 2005). The degree of weight loss during pres-
ervation is a function of specimen size, which probably explains
different responses to preservation of sexes. In absolute terms
(g), larger specimens (females) lost more mass than smaller ones
(males), yet the proportional difference (%) was negligible (Suppl.

Female --=
Male

2.8

opie

2.6 2.8
In femur length (mm)

2.6

Female -

Male ote

2.8

2.6 2.8
In femur length (mm)

2.6

Fig. 4. Length-to-mass relationships in three flightless New Zealand grasshopper species showing the influence of elevation and sexual
dimorphism. Fresh mass—femur length (A-C) and dry mass—femur length (D-F). Length-mass relationships are shown on natural loga-
rithmic axes (In). Sample sites (BR1 to BR5) indicating five sites in ~100-m elevation intervals from 1,383 to 1,817 m asl. Lines represent
the best-fit from standardized major axis regressions. Credible intervals are omitted for clarity. Some regression lines overlie each other.

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

98

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

Table 4. Model selection showing the best-fitted models (AICc in bold) for predicting both fresh mass and dry mass from femur length
in three flightless New Zealand grasshopper species. Abbreviations: K = number of parameters, AICc = Akaike’s information criterion
corrected for sample size, wi = Akaike weight, LL = Log-Likelihood, R? = marginal R?. Model parameters of the best-fitting models
(AAICc < 2) used for predictions are shown in Suppl. material 1: Appendix 4.

Model formulae K

AAICc wi LL R?
(a) fresh mass (FM) as a function of femur length (FL)

Species
Brachaspis nivalis In(FM)~In(FL) *Sex+(1|Site)
In(FM)~In(FL)+Sex+ (In(FL) |Site)
In(FM)~In(FL)+Sex+(1|Site)
In(FM)~In(FL)+(1 |Site)
In(FM)~1+(1|Site)
In(FM)~In(FL)+(1|Site)
In(FM)~In(FL)+Sex+(1|Site)
In(FM)~In(FL) *Sex+(1|Site)
In(FM)~In(FL)+Sex-+ (In(FL) |Site)
In(FM)~1+(1|Site)
In(FM)~In(FL)+Sex+(1|Site)
In(FM)~In(FL)+(1 |Site)
In(FM)~In(FL) *Sex+(1|Site)
In(FM)~In(FL)+Sex+ (In(FL) |Site)
In(FM)~1+(1|Site)

(b) dry mass (DM) as a function of femur length (FL)
In(DM)-In(FL)+(1|Site)
In(DM)-~In(FL)+Sex+(1|Site)
In(DM)-In(FL) *Sex+(1|Site)
In(DM)-~In(FL)+Sex+(FL|Site)
In(DM)~1+(1|Site)
In(DM)-In(FL)+Sex+(1|Site)
In(DM)-~In(FL) *Sex+(1|Site)
In(DM)-In(FL)+(1|Site)
In(DM)-~In(FL)+Sex+ (In(FL) |Site)
In(DM)~1+(1|Site)
In(DM)-In(FL)+(1|Site)
In(DM)-~In(FL)+Sex+(1|Site)
In(DM)-In(FL)+Sex-+ (In(FL) |Site)
In(DM)-In(FL) *Sex+(1|Site)

Paprides nitidus

Sigaus australis

WND PF Own aun BW BUN D

Brachaspis nivalis

Paprides nitidus

Sigaus australis

WAN a PWN BDNWN DU &

AICc
-284.34 0.00 0.44 148.51 0.968
-283.44 0.90 0.28 149.18 0.969
-283.22 1.12 0.25 146.85 0.967
-278.56 5.78 0.02 143.44 0.965
149.94 434.28 0.00 -71.88 0.111
-328.73 0.00 0.64 169.58 0.976
-327.15 1.58 0.29 169.88 0.981
-324.43 4.30 0.07 169.62 0.981
-296.49 32.24 0.00 152.38 0.981
247.53 576.26 0.00 -120.68 0.000
-180.21 0.00 0.65 95.46 0.987
-178.53 1.68 0.28 95.77 0.982
-175.85 4.36 0.07 95.61 0.987
-156.20 24.01 0.00 82.34 0.987
204.43 384.63 0.00 -99.07 0.000
-45.22 0.00 0.48 27.05 0.904
-44.42 0.80 0.32 27.89 0.902
-43.14 2.08 0.17 28.55 0.904
-39.14 6.08 0.02 27.90 0.903
66.45 111.67 0.00 -29.96 0.000
-69.02 0.00 0.53 40.21 0.962
-68.45 0.57 0.40 41.23 0.964
-67.97 1.05 0.03 35.94 0.955
-63.98 5.04 0.04 40.13 0.963
85.46 154.48 0.00 -39.46 0.000
-28.96 0.00 0.65 18.93 0.955
-26.75 2.21 0.21 19.06 0.955
-24.78 4.19 0.08 20.72 0.960
-24.27 4.70 0.06 19.11 0.956
120.31 149.28 0.00 -56.90 0.000

In(DM)~1+(1|Site)

material 1: Appendix 2). These results agree with previous studies
(e.g., Wetzel et al. 2005, Paxton 2013); however, additional factors,
such as surface area—volume ratio, environmental conditions, and
concentration and volume of preservative, may also influence the
leaching process (Leuven et al. 1985, Paxton 2013).

Studies of the mass-to-mass relationships of terrestrial insects are
scarce (e.g., Edwards 1996, Penell et al. 2018). Here, we found that
preserved mass was a prime predictor of body mass across all three
grasshopper species, especially when predicting fresh mass (Tables 1,
2). Inter-individual differences during the drying process seemed to
challenge model accuracy and fit when predicting dry mass. Visual
inspection of dry mass-preserved mass regressions indicates unex-
plained size-related variance, meaning higher residual error in large
individuals across all species. This suggests that inter-individual dif-
ferences in body composition (e.g., water, carbohydrates, protein,
and fat content) and condition (nutritional and reproductive status)
may be important factors explaining such variance.

The choice of a robust linear size trait is an important consider-
ation for accurate mass estimates when applying allometric scaling
regressions (Gaston and Blackburn 2000, Moretti et al. 2017). Here,
we showed that femur length strongly correlates with other body
dimensions and body mass measures in all three grasshopper spe-
cies, having by itself a high predictive power when estimating body
mass at the species level, especially when predicting fresh mass
(Tables 3, 4). Femur length has previously been shown to have a

linear relationship to body length in one of these species, which is
in turn linearly related to body mass (Batcheler 1967). However, in
all cases, body mass predictions based on preserved mass were sub-
stantially more accurate (Table 5), with prediction errors < 0.018 g
(against < 0.050 g for predictions based on femur length). Thus,
it seems sensible to use preserved mass as a predictor when basic
knowledge of the effects of preservation method on body mass is
available. Otherwise, femur length is the metric to be used, as more
than 90% of the variance in body mass was described by this trait in
all cases (Tables 3, 4). The addition of alternative body dimensions
during the modeling process would result in marginal improve-
ment of prediction accuracy but would substantially increase the
time needed for processing samples (e.g., Sohlstrém et al. 2018).
As expected, sex was sometimes retained as an informative
predictor of body mass when used in addition to or as an
interaction with femur length. This is not surprising given that
adult females of these grasshopper species are approximately
three times as heavy as adult males. Including sex generally
increased model fit (Tables 2, 4); however, its inclusion did not
produce better estimates than traditional mass-to-mass and
allometric scaling regressions (Table 5; Fig. 5). Similar allometric
relationships have been found in bees and hoverflies (Kendall et al.
2019) where sex was influential in the fit of the models but not in
their predictive power. Likewise, the use of sex-specific regressions
did not produce better mass estimates than simple regressions for

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

i. ae425 0
ri m4 s 8
w w)
© m 0.
S E = E
x f=
m7) M 0.75 ra >
2 2 =| G02
c=) TG
x*) a2)
® ® 0.50 £ =
oa) _ [s) oO
oO oO — = O41
= = a=) a=)
E 3 : :
& 0.25
a oW a a :

0.1 0.2 0.3 0.4
Measured dry mass (g)

0.1 0.2 0.3 0.4
Measured dry mass (g)

0.25 050 O75 1.00 1.25
Measured fresh mass (q)

0.25 0.50 0.75 1.00 1.25
Measured fresh mass (g)

° °
hh ow

Predicted fresh mass (g)
Predicted dry mass (g)
Predicted dry mass (g)

°

Predicted fresh mass (g)

0.3
Measured dry mass (g)

0.1 0.2 0.3 0.4

0.4
Measured dry mass (g)

0.1 0.2

0.50
Measured fresh mass (g)

0.25 0.50 0.75 0.75 1.00 1.25

1.00
Measured fresh mass (g)

1.25 0.25

Fig. 5. High predictability observed when comparing measured and predicted body mass using type-II linear regression with a major
axis approach. Predictions based on preserved mass (A-D) and femur length (E-H) pooling data from three flightless New Zealand
grasshopper species: Brachaspis nivalis (pink circles), Paprides nitidus (green triangles), and Sigaus australis (blue squares). The expected x
= y relationship is shown in dashed black line, and the observed is shown in solid grey line. Predictions from standardized major axis

regressions (SMA) and linear mixed-effects models (LMM) are shown. Note that in most cases fitting lines overlap.

European spiders (Penell et al. 2018). Although the use of models
accounting for sexual size dimorphisms is desirable, it seems that,
at least for our dataset, general regressions led to better estimates
than more complex models. While femur length was an accurate
predictor of body mass across species, it became less reliable when
comparing sexes within a given species, which can be related to
the fact that, contrary to body mass, femur length remains fixed
throughout adult life. Adult body size variation in structural linear

Table 5. Details of statistical models (type II linear regression
with a major axis) testing the relationships between predicted and
measured body mass in three flightless New Zealand grasshopper
species. Predictions are based on mass-to-mass ratios and scaling
parameters from standardized major axis regressions (SMA) and
linear mixed-effects models (LMM). The R? values, estimated in-
tercept, and slope (95% confidence intervals) are given.

traits is affected by environmental factors during development Monel Sample) pS ner ceP cn Slope(c Pavalue
(Davidowitz et al. 2004). Therefore, intraspecific changes in the vais
A ’ ‘ " : (a) Preserved mass to fresh mass

average value of adult structural size traits will require changes in jy end ratio 368 0.998 0.0044 gam 0.993 agp apm < 0.001
size and structure at the population level (Bailey et al. 2020). Pa 368 0.998 0.004, sioe-omm) 1-005 amigo, < 0.001

The difficulty of accurately predicting intraspecific body size (4) preserved mass to drvaniaee ean ‘it
variation based on co-varying linear traits is not new. The lack of = py-pMratio 150. 0.957 D006 reat OSS, iets |S x0.001
predictive power has previously been explained in terms of traits [Mm 150 0.958 LOOT pr nk 1.032 eset < 0.001
varying in response to environmental conditions during develop- —_(c) femur length to fresh mass
ment (Hagen and Dupont 2013, Kendall et al. 2019). Thus, de- SMA 368) 50.9657 £0,006, 95s, VOLS win =< 0.008
composing a multidimensional trait, such as body size, into lin- LMM 3680 _ 80963 0,031 nmin. | PROS6: neha = 10.001
ear measures seems insufficient to capture intraspecific body size (4) femur length to dry mass
variation. Indeed, body size in the broadest sense is closely linked SMA 150 0.898 -0.004, 6.413 6.002) 1-934 (575-1092) < 9-001
to the volume of an organism, which in linear terms is described MM B30, ODL) 0:80 9 eiore-c.o02) ot O88 dnt rai = 50-001

by length, width, and height (Moretti et al. 2017, Sohlstrém et al.
2018). With this in mind, predictions based on models incorporat-
ing complementary morphological traits directly related to width
and/or height (e.g., femur length in addition to pronotum width)
would improve intraspecific body mass prediction accuracy, and
thus the applicability of allometric scaling for exploring the eco-
logical implications of widespread phenomena, such as sexual
size dimorphism. Given that the sexes commonly respond differ-
ently to environmental shifts, a considerable amount of the un-
explained intraspecific variation observed here may be related to
sexual differences in body size plasticity (e.g., Stillwell et al. 2010).

The slope parameter B (power coefficient) of our femur length
regressions ranged between 2.152 and 3.293 for fresh mass and
between 2.544 and 3.425 for dry mass, thus being close to 3 as ex-
pected for animals with isometric growth (Suter and Stratton 2011).
These values are higher than those from pre-existing allometric
models (Schoener 1980) for tropical orthopterans (8 = 1.65-1.96
for dry mass estimates), further supporting differences in slopes be-
tween insects from different climatic zones: tropical insects usually
have smaller gradients than temperate ones (Schoener 1980). Inter-
estingly, the slopes of temperate grasshoppers from North America

JOURNAL OF ORTHOPTERA RESEARCH 2022, 31(1)

100

(Sabo et al. 2002) are around the lower limit of those reported here
for New Zealand grasshoppers (fB = 2.274 for dry mass estimates).
However, regression parameters for North American grasshoppers
were obtained using body length as a co-variable, thus preventing
reliable comparison, as allometric relationships often differ be-
tween traits. As noted above, the use of body length in allometric
studies on grasshoppers is not recommended, as this trait is dif-
ficult to measure accurately, thus jeopardizing model predictive
power. Therefore, we expect our regressions to be highly applicable
to ecological studies on New Zealand grasshoppers.

One source of potential error in our models is intraspecific re-
gional variation in body size. This limitation can be problematic
because scaling relationships in terrestrial insects, and thus, their
regression parameters, are likely to vary geographically if popula-
tions’ body size evolve independently of one another depending on
local conditions (e.g., Johnston and Cunjak 1999, Sohlstr6m et al.
2018, Kendall et al. 2019). New Zealand grasshoppers exhibit vari-
ations in body size among populations inhabiting elevational and
latitudinal gradients (Bigelow 1967, Staples 1967, Mason 1970).
By including size data from specimens collected on an elevational
gradient in our models, we expect to have improved model robust-
ness and reduced, at least in part, the effects of geographic size vari-
ation on their predictive power (Figs 3, 4). Variation in response
to sampling season is expected to represent an additional source
of error in our models, as average body size can change from year
to year at the same site due to differences in environmental condi-
tions (Bigelow 1967). Therefore, the performance of our models
could be affected when predicting mass estimates from individuals
with size measures far outside the trait ranges reported here.

Ecological applications. —Here we show that, for New Zealand
grasshoppers, two easy-to-measure, non-destructive, and highly
repeatable size estimates (i.e., preserved mass and femur length)
are good predictors of other difficult-to-measure but ecologically
meaningful size traits, such as fresh and dry mass. Many ecological
disciplines typically require body mass data to relate body size to
a range of ecological attributes. For example, body mass has been
proposed as a suitable metric for testing ecogeographic patterns,
such as Bergmann’s rule (Blackburn et al. 1999). Since body mass
is universally comparable, it is the metric of choice in macroeco-
logical studies interested in body size variation or size-dependent
ecological processes (Gaston and Blackburn 2000). Body size es-
timates for New Zealand grasshoppers are more frequently avail-
able as body size dimensions (but see Batcheler 1967), making
mass-to-mass and length—-mass regression models useful for in-
creasing our ability to further explore the ecological implications
of body size.

Body mass estimates from scaling regressions have proven use-
ful for studying aspects shaping arthropod communities includ-
ing biomass production (e.g., Eklof et al. 2017, Penell et al. 2018,
Kinsella et al. 2020) and abundance (White et al. 2007). Tradi-
tionally, size-abundance relationships rely on fresh body mass
of organisms (White et al. 2007, Sohlstrém et al. 2018), which
is not available for most species. Thus, mass estimates from scal-
ing regressions will alleviate this limitation. Given the apparent
linear relationship between body size and consumption rate in
the grasshopper species we examined (White 1975), indirect body
mass estimates from length-mass regressions could also be used
to predict herbivore impact on plant communities. For example,
grasshopper dry mass correlates negatively with plant biomass in
the field (Moretti et al. 2013), providing a potential trait for pre-
dicting plant consumption (Deraison et al. 2014).

EL. MEZA-JOYA, M. MORGAN-RICHARDS AND S.A. TREWICK

Recently, declines in body size have been proposed as a general
response to anthropogenic climate change in both endothermic
and ectothermic animals (Gardner et al. 2011). Examining trends
in body size requires the use of consistent size measures, and
unfortunately, data often come as different size proxies, thereby
hindering comparisons (Bailey et al. 2020). Body mass estimates
from scaling regressions have helped to overcome this limitation
by providing a tool for making size metrics from different sources
(e.g., museum specimens, published datasets, and fresh sampling)
comparable, so that tests of body size responses to climate change
and warming temperatures can be performed (e.g., Tseng et all.
2018). This approach has proven useful for studying the trends
and drivers of the change in the biomass of flying insects over time
and space (e.g., Macgregor et al. 2019, Kinsella et al. 2020) and can
now be used to estimate the body mass of New Zealand grasshop-
pers from historical abundance datasets (e.g., White 1975, White
and Sedcole 1991).

Acknowledgements

The authors thank Broken River Ski Area and its manager, Dr
Claire Newell, who allowed us access to the field site, and the
New Zealand Department of Conservation for granting collecting
permits (Authorization Number: 49878-RES and 97397-FLO). We
also acknowledge our field partners: Mari Nakano, Evans Effah,
and Andrea Clavijo. This research was supported by a grant from
the Orthopterists’ Society's Theodore J. Cohn Research Fund and
a doctoral scholarship from Massey University (awarded to FLMJ).
The manuscript was improved by constructive feedback from
Derek Woller and Maria Celeste Scattolini.

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Supplementary material 1

Author: Fabio Leonardo Meza-Joya, Mary Morgan-Richards,

Steven A. Trewick

Data type: docx file

Explanation note: Appendix 1. Measurement repeatability
based on repeated measures of body size traits from the
same specimens in three New Zealand grasshopper species.
Appendix 2. Effect of the preservation method (i.e, 95%
ethanol) on body mass by comparing mass estimates between
live (fresh mass) and preserved states (preserved mass after
two and four months of preservation) in three New Zealand
grasshopper species. Appendix 3. Intraspecific relationships
between preserved mass and both fresh and dry mass in three
New Zealand grasshopper species. Appendix 4. Regression
parameters for the best-fitted linear mixed-effect models for
body mass prediction based on preserved mass (Table 2) and
femur length (Table 4) in three New Zealand grasshopper
species. Appendix 5. Relationships between mass estimates (g)
and body dimensions (mm) in three New Zealand grasshopper
species. Appendix 6. Non-linear models fitted to describe
intraspecific allometric relationships between mass estimates
(FM and DM) and femur length (FL) in three New Zealand
grasshopper species.

Copyright notice: This dataset is made available under the Open
Database License (http://opendatacommons.org/licenses/
odbl/1.0/). The Open Database License (ODbL) is a license
agreement intended to allow users to freely share, modify,
and use this Dataset while maintaining this same freedom
for others, provided that the original source and author(s)
are credited.

Link: https://doi.org/10.3897/jor.31.79819.suppl1

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